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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.SpectralBiclustering</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-spectralbiclustering">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.SpectralBiclustering</span></code></a></li>
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  <div class="section" id="sklearn-cluster-spectralbiclustering">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a>.SpectralBiclustering<a class="headerlink" href="#sklearn-cluster-spectralbiclustering" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cluster.SpectralBiclustering">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.cluster.</code><code class="sig-name descname">SpectralBiclustering</code><span class="sig-paren">(</span><em class="sig-param">n_clusters=3</em>, <em class="sig-param">method='bistochastic'</em>, <em class="sig-param">n_components=6</em>, <em class="sig-param">n_best=3</em>, <em class="sig-param">svd_method='randomized'</em>, <em class="sig-param">n_svd_vecs=None</em>, <em class="sig-param">mini_batch=False</em>, <em class="sig-param">init='k-means++'</em>, <em class="sig-param">n_init=10</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cluster/_bicluster.py#L308"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering" title="Permalink to this definition">¶</a></dt>
<dd><p>Spectral biclustering (Kluger, 2003).</p>
<p>Partitions rows and columns under the assumption that the data has
an underlying checkerboard structure. For instance, if there are
two row partitions and three column partitions, each row will
belong to three biclusters, and each column will belong to two
biclusters. The outer product of the corresponding row and column
label vectors gives this checkerboard structure.</p>
<p>Read more in the <a class="reference internal" href="../biclustering.html#spectral-biclustering"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>n_clusters</strong><span class="classifier">integer or tuple (n_row_clusters, n_column_clusters)</span></dt><dd><p>The number of row and column clusters in the checkerboard
structure.</p>
</dd>
<dt><strong>method</strong><span class="classifier">string, optional, default: ‘bistochastic’</span></dt><dd><p>Method of normalizing and converting singular vectors into
biclusters. May be one of ‘scale’, ‘bistochastic’, or ‘log’.
The authors recommend using ‘log’. If the data is sparse,
however, log normalization will not work, which is why the
default is ‘bistochastic’. CAUTION: if <code class="docutils literal notranslate"><span class="pre">method='log'</span></code>, the
data must not be sparse.</p>
</dd>
<dt><strong>n_components</strong><span class="classifier">integer, optional, default: 6</span></dt><dd><p>Number of singular vectors to check.</p>
</dd>
<dt><strong>n_best</strong><span class="classifier">integer, optional, default: 3</span></dt><dd><p>Number of best singular vectors to which to project the data
for clustering.</p>
</dd>
<dt><strong>svd_method</strong><span class="classifier">string, optional, default: ‘randomized’</span></dt><dd><p>Selects the algorithm for finding singular vectors. May be
‘randomized’ or ‘arpack’. If ‘randomized’, uses
<a class="reference internal" href="sklearn.utils.extmath.randomized_svd.html#sklearn.utils.extmath.randomized_svd" title="sklearn.utils.extmath.randomized_svd"><code class="xref py py-func docutils literal notranslate"><span class="pre">randomized_svd</span></code></a>, which may be faster
for large matrices. If ‘arpack’, uses
<code class="docutils literal notranslate"><span class="pre">scipy.sparse.linalg.svds</span></code>, which is more accurate, but
possibly slower in some cases.</p>
</dd>
<dt><strong>n_svd_vecs</strong><span class="classifier">int, optional, default: None</span></dt><dd><p>Number of vectors to use in calculating the SVD. Corresponds
to <code class="docutils literal notranslate"><span class="pre">ncv</span></code> when <code class="docutils literal notranslate"><span class="pre">svd_method=arpack</span></code> and <code class="docutils literal notranslate"><span class="pre">n_oversamples</span></code> when
<code class="docutils literal notranslate"><span class="pre">svd_method</span></code> is ‘randomized`.</p>
</dd>
<dt><strong>mini_batch</strong><span class="classifier">bool, optional, default: False</span></dt><dd><p>Whether to use mini-batch k-means, which is faster but may get
different results.</p>
</dd>
<dt><strong>init</strong><span class="classifier">{‘k-means++’, ‘random’ or an ndarray}</span></dt><dd><p>Method for initialization of k-means algorithm; defaults to
‘k-means++’.</p>
</dd>
<dt><strong>n_init</strong><span class="classifier">int, optional, default: 10</span></dt><dd><p>Number of random initializations that are tried with the
k-means algorithm.</p>
<p>If mini-batch k-means is used, the best initialization is
chosen and the algorithm runs once. Otherwise, the algorithm
is run for each initialization and the best solution chosen.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><p>The number of jobs to use for the computation. This works by breaking
down the pairwise matrix into n_jobs even slices and computing them in
parallel.</p>
<p><code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None (default)</span></dt><dd><p>Used for randomizing the singular value decomposition and the k-means
initialization. Use an int to make the randomness deterministic.
See <a class="reference internal" href="../../glossary.html#term-random-state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>rows_</strong><span class="classifier">array-like, shape (n_row_clusters, n_rows)</span></dt><dd><p>Results of the clustering. <code class="docutils literal notranslate"><span class="pre">rows[i,</span> <span class="pre">r]</span></code> is True if
cluster <code class="docutils literal notranslate"><span class="pre">i</span></code> contains row <code class="docutils literal notranslate"><span class="pre">r</span></code>. Available only after calling <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</dd>
<dt><strong>columns_</strong><span class="classifier">array-like, shape (n_column_clusters, n_columns)</span></dt><dd><p>Results of the clustering, like <code class="docutils literal notranslate"><span class="pre">rows</span></code>.</p>
</dd>
<dt><strong>row_labels_</strong><span class="classifier">array-like, shape (n_rows,)</span></dt><dd><p>Row partition labels.</p>
</dd>
<dt><strong>column_labels_</strong><span class="classifier">array-like, shape (n_cols,)</span></dt><dd><p>Column partition labels.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<ul class="simple">
<li><p>Kluger, Yuval, et. al., 2003. <a class="reference external" href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.1608">Spectral biclustering of microarray
data: coclustering genes and conditions</a>.</p></li>
</ul>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">SpectralBiclustering</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="gp">... </span>              <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clustering</span> <span class="o">=</span> <span class="n">SpectralBiclustering</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clustering</span><span class="o">.</span><span class="n">row_labels_</span>
<span class="go">array([1, 1, 1, 0, 0, 0], dtype=int32)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clustering</span><span class="o">.</span><span class="n">column_labels_</span>
<span class="go">array([0, 1], dtype=int32)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clustering</span>
<span class="go">SpectralBiclustering(n_clusters=2, random_state=0)</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.fit" title="sklearn.cluster.SpectralBiclustering.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X[, y])</p></td>
<td><p>Creates a biclustering for X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_indices" title="sklearn.cluster.SpectralBiclustering.get_indices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_indices</span></code></a>(self, i)</p></td>
<td><p>Row and column indices of the i’th bicluster.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_params" title="sklearn.cluster.SpectralBiclustering.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_shape" title="sklearn.cluster.SpectralBiclustering.get_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_shape</span></code></a>(self, i)</p></td>
<td><p>Shape of the i’th bicluster.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.get_submatrix" title="sklearn.cluster.SpectralBiclustering.get_submatrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_submatrix</span></code></a>(self, i, data)</p></td>
<td><p>Return the submatrix corresponding to bicluster <code class="docutils literal notranslate"><span class="pre">i</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.SpectralBiclustering.set_params" title="sklearn.cluster.SpectralBiclustering.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.cluster.SpectralBiclustering.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">n_clusters=3</em>, <em class="sig-param">method='bistochastic'</em>, <em class="sig-param">n_components=6</em>, <em class="sig-param">n_best=3</em>, <em class="sig-param">svd_method='randomized'</em>, <em class="sig-param">n_svd_vecs=None</em>, <em class="sig-param">mini_batch=False</em>, <em class="sig-param">init='k-means++'</em>, <em class="sig-param">n_init=10</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cluster/_bicluster.py#L421"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.SpectralBiclustering.biclusters_">
<em class="property">property </em><code class="sig-name descname">biclusters_</code><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.biclusters_" title="Permalink to this definition">¶</a></dt>
<dd><p>Convenient way to get row and column indicators together.</p>
<p>Returns the <code class="docutils literal notranslate"><span class="pre">rows_</span></code> and <code class="docutils literal notranslate"><span class="pre">columns_</span></code> members.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.SpectralBiclustering.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cluster/_bicluster.py#L108"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a biclustering for X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd></dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.SpectralBiclustering.get_indices">
<code class="sig-name descname">get_indices</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">i</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L477"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_indices" title="Permalink to this definition">¶</a></dt>
<dd><p>Row and column indices of the i’th bicluster.</p>
<p>Only works if <code class="docutils literal notranslate"><span class="pre">rows_</span></code> and <code class="docutils literal notranslate"><span class="pre">columns_</span></code> attributes exist.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>i</strong><span class="classifier">int</span></dt><dd><p>The index of the cluster.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>row_ind</strong><span class="classifier">np.array, dtype=np.intp</span></dt><dd><p>Indices of rows in the dataset that belong to the bicluster.</p>
</dd>
<dt><strong>col_ind</strong><span class="classifier">np.array, dtype=np.intp</span></dt><dd><p>Indices of columns in the dataset that belong to the bicluster.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.SpectralBiclustering.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.SpectralBiclustering.get_shape">
<code class="sig-name descname">get_shape</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">i</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L499"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_shape" title="Permalink to this definition">¶</a></dt>
<dd><p>Shape of the i’th bicluster.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>i</strong><span class="classifier">int</span></dt><dd><p>The index of the cluster.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>shape</strong><span class="classifier">(int, int)</span></dt><dd><p>Number of rows and columns (resp.) in the bicluster.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.SpectralBiclustering.get_submatrix">
<code class="sig-name descname">get_submatrix</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">i</em>, <em class="sig-param">data</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L515"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.get_submatrix" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the submatrix corresponding to bicluster <code class="docutils literal notranslate"><span class="pre">i</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>i</strong><span class="classifier">int</span></dt><dd><p>The index of the cluster.</p>
</dd>
<dt><strong>data</strong><span class="classifier">array</span></dt><dd><p>The data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>submatrix</strong><span class="classifier">array</span></dt><dd><p>The submatrix corresponding to bicluster i.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>Works with sparse matrices. Only works if <code class="docutils literal notranslate"><span class="pre">rows_</span></code> and
<code class="docutils literal notranslate"><span class="pre">columns_</span></code> attributes exist.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.cluster.SpectralBiclustering.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.SpectralBiclustering.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-cluster-spectralbiclustering">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.SpectralBiclustering</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-spectralbiclustering" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to generate a checkerboard dataset and bicluster it using the Spe..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_spectral_biclustering_thumb.png" src="../../_images/sphx_glr_plot_spectral_biclustering_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/bicluster/plot_spectral_biclustering.html#sphx-glr-auto-examples-bicluster-plot-spectral-biclustering-py"><span class="std std-ref">A demo of the Spectral Biclustering algorithm</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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